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Found 7 papers, 2 papers with code
Fairness-Aware Structured Pruning in Transformers
The increasing size of large language models (LLMs) has introduced challenges in their training and inference.
Should We Attend More or Less? Modulating Attention for Fairness
In this work, we investigate the role of attention, a widely-used technique in current state-of-the-art NLP models, in the propagation of social biases.
Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness
The fairness achieved by our method surpasses that of data augmentation on three text classification datasets, using no more than half of the examples in the augmented dataset.
Fast Strain Estimation and Frame Selection in Ultrasound Elastography using Machine Learning
In the inference stage, we use dynamic programming (DP) to compute an initial displacement estimate of around 1% of the samples, and then decompose this sparse displacement into a linear combination of the 12 displacement modes.
Automatic Frame Selection using CNN in Ultrasound Elastography
Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force.
Automatic Frame Selection Using MLP Neural Network in Ultrasound Elastography
This work focuses on strain imaging in quasi-static elastography, where the tissue undergoes slow deformations and strain images are estimated as a surrogate for elasticity modulus.
Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysis
Time delay estimation (TDE) is a critical and challenging step in all ultrasound elastography methods.
